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<h1 id="tensor-operations">Tensor Operations</h1>
<p>In this section are listed all of the <strong>Tensor Operation</strong> methods.</p>
<h2 id="torchadd">torch.add</h2>
<pre><code>torch.add(a,
          b) → Tensor
</code></pre>
<ul>
<li>If both tensors are scalars, the simple sum is returned.</li>
<li>If one tensor is a scalar, the element-wise sum of the scalar and the tensor is returned.</li>
<li>If tensors both are n-dimensional tensors, JS-PyTorch will attempt to broadcast their shapes, and add them element-wise.</li>
</ul>
<p>Parameters</p>
<ul>
<li><strong>a (Tensor | number)</strong> - Input Tensor or number.</li>
<li><strong>b (Tensor | number)</strong> - Other Tensor or number.</li>
</ul>
<p>Example</p>
<pre><code class="language-javascript">&gt;&gt;&gt; let a = torch.tensor([[1,1,2,3],
                          [6,7,8,9]]);
&gt;&gt;&gt; let b = torch.tensor([1]);
&gt;&gt;&gt; let c = torch.add(a,b);
&gt;&gt;&gt; c.data;
//[[2,2,3,4],
// [7,8,9,10]]
&gt;&gt;&gt; b = torch.tensor([[0],
                      [2]]);
&gt;&gt;&gt; c = torch.add(a,b);
&gt;&gt;&gt; c.data;
//[[1,1,2,3],
// [8,9,10,11]]
&gt;&gt;&gt; b = torch.tensor([[0,0,0,0],
                      [0,0,100,0]]);
&gt;&gt;&gt; c = torch.add(a,b);
&gt;&gt;&gt; c.data;
//[[1,1,2,3],
// [6,7,108,9]]
</code></pre>
<blockquote>
<p><strong>Note:</strong> <code>torch.add(a, b)</code> is the same as <code>a.add(b)</code>.</p>
</blockquote>
<p><br></p>
<h2 id="torchsub">torch.sub</h2>
<pre><code>torch.sub(a,
          b) → Tensor
</code></pre>
<ul>
<li>If both tensors are scalars, the simple subtraction is returned.</li>
<li>If one tensor is a scalar, the element-wise subtraction of the scalar and the tensor is returned.</li>
<li>If tensors both are n-dimensional tensors, JS-PyTorch will attempt to broadcast their shapes, and subtract them element-wise.</li>
</ul>
<p>Parameters</p>
<ul>
<li><strong>a (Tensor | number)</strong> - Input Tensor or number.</li>
<li><strong>b (Tensor | number)</strong> - Other Tensor or number.</li>
</ul>
<p>Example</p>
<pre><code class="language-javascript">&gt;&gt;&gt; let a = torch.tensor([[1,1,2,3],
                          [6,7,8,9]]);
&gt;&gt;&gt; let b = torch.tensor([1]);
&gt;&gt;&gt; let c = torch.sub(a,b);
&gt;&gt;&gt; c.data;
//[[0,0,1,2],
// [5,6,7,8]]
&gt;&gt;&gt; b = torch.tensor([[0],
                      [2]]);
&gt;&gt;&gt; c = torch.sub(a,b);
&gt;&gt;&gt; c.data;
//[[1,1,2,3],
// [4,5,6,7]]
&gt;&gt;&gt; b = torch.tensor([[0,0,0,0],
                      [0,0,8,0]]);
&gt;&gt;&gt; c = torch.sub(a,b);
&gt;&gt;&gt; c.data;
//[[1,1,2,3],
// [6,7,0,9]]
</code></pre>
<blockquote>
<p><strong>Note:</strong> <code>torch.sub(a, b)</code> is the same as <code>a.sub(b)</code>.</p>
</blockquote>
<p><br></p>
<h2 id="torchneg">torch.neg</h2>
<pre><code>torch.neg(a) → Tensor
</code></pre>
<p>Returns the element-wise opposite of the given Tensor.</p>
<p>Parameters</p>
<ul>
<li><strong>a (Tensor | number)</strong> - Input Tensor or number.</li>
</ul>
<p>Example</p>
<pre><code class="language-javascript">&gt;&gt;&gt; let a = torch.tensor([1]);
&gt;&gt;&gt; let b = torch.neg(a);
&gt;&gt;&gt; c.data;
// [-1]
&gt;&gt;&gt; a = torch.tensor([-3]);
&gt;&gt;&gt; b = torch.neg(a);
&gt;&gt;&gt; c.data;
// [3]
&gt;&gt;&gt; a = torch.tensor([[0,1,0,-1],
                      [-3,2,1,0]]);
&gt;&gt;&gt; b = torch.neg(a);
&gt;&gt;&gt; c.data;
//[[0,-1,0,1],
// [3,-2,-1,0]]
</code></pre>
<blockquote>
<p><strong>Note:</strong> <code>torch.neg(a)</code> is the same as <code>a.neg()</code>.</p>
</blockquote>
<p><br></p>
<h2 id="torchmul">torch.mul</h2>
<pre><code>torch.mul(a,
          b) → Tensor
</code></pre>
<ul>
<li>If both tensors are scalars, the simple dot product is returned.</li>
<li>If one tensor is a scalar, the element-wise product of the scalar and the tensor is returned.</li>
<li>If tensors both are n-dimensional tensors, JS-PyTorch will attempt to broadcast their shapes, and multiply them element-wise.</li>
</ul>
<p>Parameters</p>
<ul>
<li><strong>a (Tensor | number)</strong> - Input Tensor or number.</li>
<li><strong>b (Tensor | number)</strong> - Other Tensor or number.</li>
</ul>
<p>Example</p>
<pre><code class="language-javascript">&gt;&gt;&gt; let a = torch.tensor([[1,1,2,3],
                          [6,7,8,9]]);
&gt;&gt;&gt; let b = torch.tensor([2]);
&gt;&gt;&gt; let c = torch.mul(a,b);
&gt;&gt;&gt; c.data;
//[[0,0,1,2],
// [5,6,7,8]]
&gt;&gt;&gt; b = torch.tensor([[0],
                      [-1]]);
&gt;&gt;&gt; c = torch.mul(a,b);
&gt;&gt;&gt; c.data;
//[[0, 0, 0, 0],
// [-6,-7,-8,-9]]
&gt;&gt;&gt; b = torch.tensor([[0,0,0,0],
                      [0,0,8,0]]);
&gt;&gt;&gt; c = torch.mul(a,b);
&gt;&gt;&gt; c.data;
//[[1,1,2,3],
// [6,7,0,9]]
</code></pre>
<blockquote>
<p><strong>Note:</strong> <code>torch.mul(a, b)</code> is the same as <code>a.mul(b)</code>.</p>
</blockquote>
<p><br></p>
<h2 id="torchdiv">torch.div</h2>
<pre><code>torch.div(a,
          b) → Tensor
</code></pre>
<ul>
<li>If both tensors are scalars, the simple division is returned.</li>
<li>If one tensor is a scalar, the element-wise division of the scalar and the tensor is returned.</li>
<li>If tensors both are n-dimensional tensors, JS-PyTorch will attempt to broadcast their shapes, and divide them element-wise.</li>
</ul>
<p>Parameters</p>
<ul>
<li><strong>a (Tensor | number)</strong> - Input Tensor or number.</li>
<li><strong>b (Tensor | number)</strong> - Other Tensor or number.</li>
</ul>
<p>Example</p>
<pre><code class="language-javascript">&gt;&gt;&gt; let a = torch.tensor([[2,-2,4,6],
                          [6,-6,8,8]]);
&gt;&gt;&gt; let b = torch.tensor([2]);
&gt;&gt;&gt; let c = torch.div(a,b);
&gt;&gt;&gt; c.data;
//[[1,-1,2,3],
// [3,-3,4,4]]
&gt;&gt;&gt; b = torch.tensor([[1],
                      [-1]]);
&gt;&gt;&gt; c = torch.div(a,b);
&gt;&gt;&gt; c.data;
//[[2,-2, 4, 6],
// [-6,6,-8,-8]]
&gt;&gt;&gt; b = torch.tensor([[1,1,1,1],
                      [1,1,16,1]]);
&gt;&gt;&gt; c = torch.div(a,b);
&gt;&gt;&gt; c.data;
//[[2,-2, 4, 6],
// [6,-6,0.5,8]]
</code></pre>
<blockquote>
<p><strong>Note:</strong> <code>torch.div(a, b)</code> is the same as <code>a.div(b)</code>.</p>
</blockquote>
<p><br></p>
<h2 id="torchmatmul">torch.matmul</h2>
<pre><code>torch.matlul(a,
             b) → Tensor
</code></pre>
<p>Performs matrix multiplication between the last two dimensions of each Tensor.
If inputs are of shape <code>[H,W]</code> and <code>[W,C]</code>, the output will have shape <code>[H,C]</code>.
If the two Tensors have more than two dimensions, they can be <strong>broadcast</strong>:</p>
<ul>
<li><code>[B,N,H,W], [B,N,W,C] =&gt; [B,N,H,C]</code></li>
<li><code>[B,N,H,W], [W,C] =&gt; [B,N,H,C]</code></li>
<li><code>[H,W], [B,N,W,C] =&gt; [B,N,H,C]</code></li>
<li><code>[B,N,H,W], [1,1,W,C] =&gt; [B,N,H,C]</code></li>
</ul>
<p>Parameters</p>
<ul>
<li><strong>a (Tensor | number)</strong> - Input Tensor.</li>
<li><strong>b (Tensor | number)</strong> - Other Tensor.</li>
</ul>
<p>Example</p>
<pre><code class="language-javascript">&gt;&gt;&gt; let a = torch.tensor([[1,1,1,2], 
                          [3,1,0,0]]); // Shape [2,4]
&gt;&gt;&gt; let b = torch.tensor([[1],
                          [0],
                          [0],
                          [0]]); // Shape [4,1]
&gt;&gt;&gt; let c = torch.matmul(a,b); // Shape [2,1]
&gt;&gt;&gt; c.data;
//[[1],
// [3]]
</code></pre>
<blockquote>
<p><strong>Note:</strong> <code>torch.matmul(a, b)</code> is the same as <code>a.matmul(b)</code>.</p>
</blockquote>
<p><br></p>
<h2 id="torchsum">torch.sum</h2>
<pre><code>torch.sum(a,
          dim,
          keepdims=false) → Tensor
</code></pre>
<p>Gets the sum of the Tensor over a specified dimension.</p>
<p>Parameters</p>
<ul>
<li><strong>a (Tensor)</strong> - Input Tensor.</li>
<li><strong>dim (integer)</strong> - Dimension to perform the sum over.</li>
<li><strong>keepdims (boolean)</strong> - Whether to keep dimensions of original tensor.</li>
</ul>
<p>Example</p>
<pre><code class="language-javascript">&gt;&gt;&gt; let a = torch.ones([4,3], false, 'gpu');
&gt;&gt;&gt; a.data;
//[[1, 1, 1],
// [1, 1, 1]
// [1, 1, 1]
// [1, 1, 1]]
&gt;&gt;&gt; let b = torch.sum(a, 0);
&gt;&gt;&gt; b.data;
// [[4, 4, 4]]
&gt;&gt;&gt; b = torch.sum(a, 1);
&gt;&gt;&gt; b.data;
// [[3],
//  [3],
//  [3],
//  [3]]
&gt;&gt;&gt; b = torch.sum(a, 0, true);
&gt;&gt;&gt; b.data;
//[[4, 4, 4],
// [4, 4, 4]
// [4, 4, 4]
// [4, 4, 4]]
</code></pre>
<blockquote>
<p><strong>Note:</strong> <code>torch.sum(a)</code> is the same as <code>a.sum()</code>.</p>
</blockquote>
<p><br></p>
<h2 id="torchmean">torch.mean</h2>
<pre><code>torch.mean(a,
          dim,
          keepdims=false) → Tensor
</code></pre>
<p>Gets the mean of the Tensor over a specified dimension.</p>
<p>Parameters</p>
<ul>
<li><strong>a (Tensor)</strong> - Input Tensor.</li>
<li><strong>dim (integer)</strong> - Dimension to get the mean of.</li>
<li><strong>keepdims (boolean)</strong> - Whether to keep dimensions of original tensor.</li>
</ul>
<p>Example</p>
<pre><code class="language-javascript">&gt;&gt;&gt; let a = torch.randint(0, 2, [2,3], false, 'gpu');
&gt;&gt;&gt; a.data;
//[[0, 1, 0],
// [1, 1, 1]]
&gt;&gt;&gt; let b = torch.mean(a, 0);
&gt;&gt;&gt; b.data;
// [[0.5, 1, 0.5]]
&gt;&gt;&gt; b = torch.mean(a, 1);
&gt;&gt;&gt; b.data;
// [[0.333333],
//  [1]]
&gt;&gt;&gt; b = torch.mean(a, 0, true);
&gt;&gt;&gt; b.data;
//[[0.5, 1, 0.5],
// [0.5, 1, 0.5]]
</code></pre>
<blockquote>
<p><strong>Note:</strong> <code>torch.mean(a)</code> is the same as <code>a.mean()</code>.</p>
</blockquote>
<p><br></p>
<h2 id="torchvariance">torch.variance</h2>
<pre><code>torch.variance(a,
          dim,
          keepdims=false) → Tensor
</code></pre>
<p>Gets the variance of the Tensor over a specified dimension.</p>
<p>Parameters</p>
<ul>
<li><strong>a (Tensor)</strong> - Input Tensor.</li>
<li><strong>dim (integer)</strong> - Dimension to get the variance of.</li>
<li><strong>keepdims (boolean)</strong> - Whether to keep dimensions of original tensor.</li>
</ul>
<p>Example</p>
<pre><code class="language-javascript">&gt;&gt;&gt; let a = torch.randint(0, 3, [3,2], false, 'gpu');
&gt;&gt;&gt; a.data;
//[[0, 2],
// [2, 1],
// [0, 1]]
&gt;&gt;&gt; let b = torch.variance(a, 0);
&gt;&gt;&gt; b.data;
// [[0.9428, 0.471404]]
&gt;&gt;&gt; b = torch.variance(a, 0, true);
&gt;&gt;&gt; b.data;
//[[0.9428, 0.471404]
// [0.9428, 0.471404]
// [0.9428, 0.471404]]
</code></pre>
<blockquote>
<p><strong>Note:</strong> <code>torch.variance(a)</code> is the same as <code>a.variance()</code>.</p>
</blockquote>
<p><br></p>
<h2 id="torchtranspose">torch.transpose</h2>
<pre><code>torch.transpose(a,
          dim1,
          dim2) → Tensor
</code></pre>
<p>Transposes the tensor along two consecutive dimensions.</p>
<p>Parameters</p>
<ul>
<li><strong>a (Tensor)</strong> - Input Tensor.</li>
<li><strong>dim1 (integer)</strong> - First dimension.</li>
<li><strong>dim2 (boolean)</strong> - Second dimension.</li>
</ul>
<p>Example</p>
<pre><code class="language-javascript">&gt;&gt;&gt; let a = torch.randint(0, 3, [3,2], false, 'gpu');
&gt;&gt;&gt; a.data;
//[[0, 2],
// [2, 1],
// [0, 1]]
&gt;&gt;&gt; let b = torch.transpose(a, -1, -2);
&gt;&gt;&gt; b.data;
//[[0, 2, 0],
// [2, 1, 1]]
</code></pre>
<blockquote>
<p><strong>Note:</strong> <code>torch.transpose(a)</code> is the same as <code>a.transpose()</code>.</p>
</blockquote>
<p><br></p>
<h2 id="torchat">torch.at</h2>
<pre><code>torch.at(a,
          dim1,
          dim2) → Tensor
</code></pre>
<p>If a single Array <code>index1</code> is passed, returns the elements in the tensor indexed by this Array: <code>tensor[index1]</code>.
If a two Arrays <code>index1</code> and <code>index2</code> are passed, returns the elements in the tensor indexed by <code>tensor[index1][index2]</code>.</p>
<p>Parameters</p>
<ul>
<li><strong>a (Tensor)</strong> - Input Tensor.</li>
<li><strong>index1 (Array)</strong> - Array containing indexes to extract data from in first dimension.</li>
<li><strong>index2 (Array)</strong> - Array containing indexes to extract data from in second dimension.</li>
</ul>
<p>Example</p>
<pre><code class="language-javascript">&gt;&gt;&gt; let a = torch.tensor([[1,1,2,3],
                [6,7,8,9]]);
&gt;&gt; let b = torch.at(a,[0,1,1], [2,0,3]);
&gt;&gt;&gt; b.data;
// [2,6,9]
&gt;&gt;&gt; b = torch.at(a,[0,1,0]);
&gt;&gt;&gt; b.data;
// [[1,1,2,3],
//  [6,7,8,9],
//  [1,1,2,3]])
</code></pre>
<blockquote>
<p><strong>Note:</strong> <code>torch.at(a)</code> is the same as <code>a.at()</code>.</p>
</blockquote>
<p><br></p>
<h2 id="torchmasked_fill">torch.masked_fill</h2>
<pre><code>torch.masked_fill(a,
                  condition,
                  value) → Tensor
</code></pre>
<p>A condition function scans the <code>a</code> tensor element-wise, returning <code>true</code> or <code>false</code>.
In places within the <code>a</code> tensor where the "condition" function returns True, we set the value to <code>value</code>.</p>
<p>Parameters</p>
<ul>
<li><strong>a (Tensor)</strong> - Input Tensor.</li>
<li><strong>condition (function)</strong> - Function that returns True or False element-wise.</li>
<li><strong>value (number)</strong> - Value to fill Tensor when condition is met.</li>
</ul>
<p>Example</p>
<pre><code class="language-javascript">&gt;&gt;&gt; let a = torch.tensor([[1,5,2,3],
                   [6,7,2,9]]);
&gt;&gt;&gt; let b = torch.masked_fill(a, mask, (el) =&gt; {return el &gt; 3}, 0);
&gt;&gt;&gt; b.data;
// [[1,0,2,3],
//  [0,0,2,0]]
</code></pre>
<blockquote>
<p><strong>Note:</strong> <code>torch.masked_fill(a)</code> is the same as <code>a.masked_fill()</code>.</p>
</blockquote>
<p><br></p>
<h2 id="torchpow">torch.pow</h2>
<pre><code>torch.pow(a,
          n) → Tensor
</code></pre>
<p>Returns tensor to element-wise power of n.</p>
<p>Parameters</p>
<ul>
<li><strong>a (Tensor)</strong> - Input Tensor.</li>
<li><strong>n (function)</strong> - Exponent.</li>
</ul>
<p>Example</p>
<pre><code class="language-javascript">&gt;&gt;&gt; let a = torch.tensor([[1,-5],
                          [6,7]]);
&gt;&gt;&gt; let b = torch.pow(a, 2);
&gt;&gt;&gt; b.data;
// [[1,25],
//  [36,49]]
</code></pre>
<blockquote>
<p><strong>Note:</strong> <code>torch.pow(a)</code> is the same as <code>a.pow()</code>.</p>
</blockquote>
<p><br></p>
<h2 id="torchsqrt">torch.sqrt</h2>
<pre><code>torch.sqrt(a) → Tensor
</code></pre>
<p>Returns element-wise square root of the tensor.</p>
<p>Parameters</p>
<ul>
<li><strong>a (Tensor)</strong> - Input Tensor.</li>
</ul>
<p>Example</p>
<pre><code class="language-javascript">&gt;&gt;&gt; let a = torch.tensor([[1,9],
                          [4,16]]);
&gt;&gt;&gt; let b = torch.sqrt(a);
&gt;&gt;&gt; b.data;
// [[1,3],
//  [2,4]]
</code></pre>
<blockquote>
<p><strong>Note:</strong> <code>torch.sqrt(a)</code> is the same as <code>a.sqrt()</code>.</p>
</blockquote>
<p><br></p>
<h2 id="torchexp">torch.exp</h2>
<pre><code>torch.exp(a) → Tensor
</code></pre>
<p>Returns element-wise exponentiation of the tensor.</p>
<p>Parameters</p>
<ul>
<li><strong>a (Tensor)</strong> - Input Tensor.</li>
</ul>
<p>Example</p>
<pre><code class="language-javascript">&gt;&gt;&gt; let a = torch.tensor([[1,2],
                          [0,-1]]);
&gt;&gt;&gt; let b = torch.exp(a);
&gt;&gt;&gt; b.data;
// [[2.71828,7.389056],
//  [1.00000,0.36788]]
</code></pre>
<blockquote>
<p><strong>Note:</strong> <code>torch.exp(a)</code> is the same as <code>a.exp()</code>.</p>
</blockquote>
<p><br></p>
<h2 id="torchlog">torch.log</h2>
<pre><code>torch.log(a) → Tensor
</code></pre>
<p>Returns element-wise natural log of the tensor.</p>
<p>Parameters</p>
<ul>
<li><strong>a (Tensor)</strong> - Input Tensor.</li>
</ul>
<p>Example</p>
<pre><code class="language-javascript">&gt;&gt;&gt; let a = torch.tensor([[1,2],
                          [0.01,3]]);
&gt;&gt;&gt; let b = torch.log(a);
&gt;&gt;&gt; b.data;
// [[0.00000,0.693147],
//  [-4.6051,1.098612]]
</code></pre>
<blockquote>
<p><strong>Note:</strong> <code>torch.log(a)</code> is the same as <code>a.log()</code>.</p>
</blockquote>












                
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